Notice bibliographique
Résumé
Chronic obstructive pulmonary disease (COPD) is currently the 12th leading cause of disability in the world and is predicted to be 5th by the year 2020 (1). In the United States alone, it has been estimated that the annual cost of morbidity and early mortality due to COPD is approximately 4.7 billion dollars (2). COPD is a complex condition in which environmental factors interact with genetic susceptibility to cause disease. Tobacco smoke is the most important environmental risk factor, and in susceptible individuals it causes an exaggerated inflammatory response that ultimately destroys the lung parenchyma (emphysema) and/or increases airway resistance by remodeling of the airway wall (3). It has long been known that the pathway varies between individuals; some patients have predominant emphysema while others can have similar degrees of airflow obstruction due to severe small airway disease with relatively preserved parenchyma, but the proportion and contribution of each to the pathogenesis of disease is still unknown. Even though recent research has advanced our understanding of COPD pathogenesis, leading to the identification of potential targets and pathways for drug development, there are still major difficulties in conducting clinical trials designed to evaluate the benefits of new drug treatment for several reasons. These reasons include (1) the lack of validated shortand intermediate-term endpoints (or surrogates) that are predictive of future hard clinical outcomes, and (2) the lack of a method to provide precise phenotypes suitable for large-scale studies. It is for these reasons that computed tomography (CT) has become such an important tool in COPD research. CT provides a noninvasive method to obtain images of the lung that look similar to anatomic assessment, and CT images themselves are densitometry maps of the lung. Therefore any change in the structure of the lung will change the densitometry of the lung and, therefore, the image. Virtually every clinical center in all regions of the world has access to a CT scanner, so it is thought that CT images should be quite easy to obtain and it should be easy to conduct large, meaningful clinical studies. However, while CT is a powerful tool, it does have some limitations and caveats for general use in clinical studies. These limitations include disagreements on the best method to analyze the lung parenchyma, no definitive study using airway wall algorithms, exposure of subjects to ionizing radiation, and the use of improperly calibrated CT scanners. In 2001 the Alpha-1 Foundation sponsored a workshop on the use of CT in longitudinal studies. That workshop resulted in recommendations on the use of CT to analyze the lung parenchyma, namely, the use of the lowest 15th percentile point of lung density (4). Since then there has been much debate surrounding CT analysis, and it was decided that another workshop was needed to readdress these issues. Specifically the workshop was designed to look at the use of CT for quantifying the lung parenchyma and the airway wall dimensions, and at how those methods could be applied to longitudinal studies and what drawbacks there may be in these approaches. This summary will only briefly discuss the broad concepts of these issues and deal specifically with the recommendations of the workshop.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».